Information Propagation in Complex Networks Structures and Dynamics

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@PhdThesis{Maertens:thesis,
  author =       "Marcus Maertens",
  title =        "Information Propagation in Complex Networks Structures
                 and Dynamics",
  school =       "Delft University of Technology",
  year =         "2018",
  address =      "Holland",
  month =        "8 " # jan,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, information propagation,
                 functional brain networks, toxicity, multi-player
                 online games, network epidemics, epidemic spreading
                 model, complex networks, symbolic regression",
  isbn13 =       "978-94-028-0907-7",
  URL =          "https://repository.tudelft.nl/islandora/object/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1",
  URL =          "https://repository.tudelft.nl/islandora/object/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1/datastream/OBJ/download",
  DOI =          "doi:10.4233/uuid:b1a1ead7-a631-4f05-b9a9-17a1be6e15e1",
  size =         "150 pages",
  abstract =     "... chapter 6 is a study on the capabilities of
                 symbolic regression for network properties. We develop
                 an automated system based on Genetic Programming which
                 is able to be trained by families of networks to learn
                 the relations between several of their properties.
                 These properties can be features of the networks like
                 the eigenvalues of their adjacency or Laplacian
                 matrices or network metrics like the network diameter
                 or the isoperimetric number. We show that the system
                 can generate approximate formulas for those metrics
                 that often give better results than previously known
                 analytic bounds. The evolved formulae for the network
                 diameter are evaluated on a selection of real-world
                 networks of different origins. The network diameter
                 bounds hop-based information propagation and is thus of
                 high importance for designing network algorithms. A
                 careful selection of training networks and network
                 features is crucial for evolving good approximate
                 formulas for the network diameter and similar
                 properties. ...",
  notes =        "Supervisor prof. dr. ir. P. F. A. Van Mieghem Section
                 6.2.2 Cartesian Genetic Programming
                 (CGP)

                 http://repository.tudelft.nl/",
}

Genetic Programming entries for Marcus Maertens

Citations